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AP 统计主要是介绍收集、分析和从数据中得出结论的主要概念和工具。
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我们提供的AP统计及其相关学科的代写，服务范围广, 其中包括但不限于:
- Statistical Inference 统计推断
- Statistical Computing 统计计算
- Advanced Probability Theory 高等楖率论
- Advanced Mathematical Statistics 高等数理统计学
- (Generalized) Linear Models 广义线性模型
- Statistical Machine Learning 统计机器学习
- Longitudinal Data Analysis 纵向数据分析
- Foundations of Data Science 数据科学基础
统计代写|AP统计作业代写代考|Exploratory Data Analysis
Together with histograms and other graphics techniques, the stem-and-leaf display is one of many useful ways of studying data in a field called exploratory data analysis (often abbreviated as EDA). John W. Tukey wrote one of the definitive books on the subject, Exploratory Data Analysis (Addison-Wesley). Another very useful reference for EDA techniques is the book Applications, Basics, and Computing of Exploratory Data Analysis by Paul F. Velleman and David C. Hoaglin (Duxbury Press). Exploratory data analysis techniques are particularly useful for detecting patterns and extreme data values. They are designed to help us explore a data set, to ask questions we had not thought of before, or to pursue leads in many directions.
EDA techniques are similar to those of an explorer. An explorer has a general idea of destination but is always alert for the unexpected. An explorer needs to assess situations quickly and often simplify and clarify them. An explorer makes pictures-that is, maps showing the relationships of landscape features. The aspects of rapid implementation,
visual displays such as graphs and charts, data simplification, and robustness (that is, analysis that is not influenced much by extreme data values) are key ingredients of EDA techniques. In addition, these techniques are good for exploration because they require very few prior assumptions about the data.
EDA methods are especially useful when our data have been gathered for general interest and observation of subjects. For example, we may have data regarding the ages of applicants to graduate programs. We don’t have a specific question in mind. We want to see what the data reveal. Are the ages fairly uniform or spread out? Are thers exesptionally young or old applicants? If there arc, we might look at other characteristics of these applicants, such as field of study. EDA methods help us quickly absorb some aspects of the data and then may lead us to ask specific questions to which we might apply methods of traditional statistics.
In contrast, when we design an experiment to produce data to answer a specific question, we focus on particular aspects of the data that are useful to us. If we want to determine the average highway gas mileage of a specific sports car, we use that model car in well-designed tests. We don’t need to worry about unexpected road conditions, poorly trained drivers, different fuel grades, sudden stops and starts, etc. Our experiment is designed to control outside factors. Consequently, we do not need to “explore” our data as much. We can often make valid assumptions about the data. Methods of traditional statistics will be very useful to analyze such data and answer our specific questions.
统计代写|AP统计作业代写代考|Stem-and-Leaf Display
Many airline passengers seem weighted down by their carry-on luggage. Just how much weight are they carrying? The carry-on luggage weights in pounds for a random sample of 40 passengers returning from a vacation to Hawaii were recorded (see Table 2-15).
To make a stem-and-leaf display, we break the digits of each data value into two parts. The left group of digits is called a stem, and the remaining group of digits on the right is called a leaf. We are free to choose the number of digits to be included in the stem.
The weights in our example consist of two-digit numbers. For a two-digit number, the stem selection is obviously the left digit. In our case, the tens digits will form the stems, and the units digits will form the leaves. For example, for the weight 12 , the stem is 1 and the leaf is 2 . For the weight 18 , the stem is again 1, but the leaf is 8. In the stem-and-leaf display, we list each possible stem once on the left and all its leaves in the same row on the right, as in Figure 2-15(a). Finally, we order the leaves as shown in Figure 2- $15(b)$.
Figure 2-15 shows a stem-and-leaf display for the weights of carry-on luggage. From the stem-and-leaf display in Figure 2-15, we see that two bags weighed $27 \mathrm{lb}$, one weighed $3 \mathrm{lb}$, one weighed $51 \mathrm{lb}$, and so on. We see that most of the weights were in the 30 -lb range, only two were less than $10 \mathrm{lb}$, and six were over $40 \mathrm{lb}$. Note that the lengths of the lines containing the leaves give the visual impression that a sideways histogram would present.
As a final step, we need to indicate the scale. This is usually done by indicating the value represented by a stem and one leaf.
There are no firm rules for selecting the group of digits for the stem. But whichever group you select, you must list all the possible stems from smallest to largest in the data collection.
统计代写|AP统计作业代写代考|Cowboys: Longevity
Cowboys: Longevity How long did real cowboys live? One answer may be found in the book The Last Cowboys by Connie Brooks (University of New Mexico Press). This delightful book presents a thoughtful sociological study of cowboys in west Texas and southeastem New Mexico around the year 1890. A sample of 32 cowboys gave the following years of longevity:
(a) Make a stem-and-leaf display for these data.
(b) Interpretation Consider the following quote from Baron von Richthofen in his Cattle Raising on the Plains of North America: “Cowboys are to be found among the sons of the best families. The truth is probably that most were not a drunken, gambling lot, quick to draw and fire their pistols.”
Does the data distribution of longevity lend credence to this quote?
Ecology: Habitat Wetlands offer a diversity of benefits. They provide a habitat for wildlife, spawning grounds for U.S. commercial fish, and renewable timber resources. In the last 200 years, the United States has lost more than half its wetlands. Environmental Almanac gives the percentage of wetlands lost in each state in the last 200 years. For the lower 48 states, the percentage loss of wetlands per state is as follows:
$\begin{array}{lllcllllll}46 & 37 & 36 & 42 & 81 & 20 & 73 & 59 & 35 & 50 \ 87 & 52 & 24 & 27 & 38 & 56 & 39 & 74 & 56 & 31 \ 27 & 91 & 46 & 9 & 54 & 52 & 30 & 33 & 28 & 35 \ 35 & 23 & 90 & 72 & 85 & 42 & 59 & 50 & 49 & \ 48 & 38 & 60 & 46 & 87 & 50 & 89 & 49 & 67 & \end{array}$
Make a stem-and-leaf display of these data. Be sure to indicate the scale. How are the percentages distributed? Is the distribution skewed? Are there any gaps?
AP统计代写
统计代写|AP统计作业代写代考|Exploratory Data Analysis
与直方图和其他图形技术一起，茎叶显示是在称为探索性数据分析（通常缩写为 EDA）的领域中研究数据的许多有用方法之一。John W. Tukey 写了一本关于该主题的权威书籍，探索性数据分析 (Addison-Wesley)。另一个对 EDA 技术非常有用的参考资料是 Paul F. Velleman 和 David C. Hoaglin（Duxbury 出版社）所著的《探索性数据分析的应用、基础和计算》一书。探索性数据分析技术对于检测模式和极端数据值特别有用。它们旨在帮助我们探索数据集，提出我们以前没有想到的问题，或者在多个方向上寻找线索。
EDA 技术类似于资源管理器的技术。探险家对目的地有一个大致的了解，但总是对意外情况保持警惕。探索者需要快速评估情况，并经常简化和澄清它们。探险家制作图片——即显示景观特征关系的地图。快速实施方面，
图形和图表等可视化显示、数据简化和稳健性（即不受极端数据值影响的分析）是 EDA 技术的关键要素。此外，这些技术非常适合探索，因为它们只需要很少的关于数据的先验假设。
EDA 方法在我们为一般兴趣和观察对象而收集数据时特别有用。例如，我们可能有关于研究生课程申请者年龄的数据。我们没有具体的问题。我们想看看数据揭示了什么。年龄是相当统一的还是分散的？他们是特别年轻还是年长的申请人？如果有弧线，我们可能会查看这些申请人的其他特征，例如研究领域。EDA 方法帮助我们快速吸收数据的某些方面，然后可能会引导我们提出可以应用传统统计方法的特定问题。
相比之下，当我们设计一个实验来产生数据来回答特定问题时，我们会关注数据中对我们有用的特定方面。如果我们想确定特定跑车的平均公路油耗，我们会在精心设计的测试中使用该模型车。我们不需要担心意外的路况、训练有素的司机、不同的燃料等级、突然停止和启动等。我们的实验旨在控制外部因素。因此，我们不需要过多地“探索”我们的数据。我们通常可以对数据做出有效的假设。传统统计方法对于分析此类数据并回答我们的具体问题将非常有用。
统计代写|AP统计作业代写代考|Stem-and-Leaf Display
许多航空公司的乘客似乎被他们的随身行李压得喘不过气来。他们承载了多少重量？记录了从假期返回夏威夷的 40 名乘客的随机样本，以磅为单位的随身行李重量（见表 2-15）。
为了进行茎叶显示，我们将每个数据值的数字分成两部分。左边的一组数字称为茎，右边的剩余数字组称为叶。我们可以自由选择要包含在词干中的位数。
我们示例中的权重由两位数组成。对于两位数的数字，词干选择显然是左边的数字。在我们的例子中，十位数字将形成茎，个位数字将形成叶子。例如，对于权重 12，茎为 1，叶为 2。对于权重 18 ，茎再次为 1，但叶为 8。在茎叶显示中，我们在左侧列出每个可能的茎，在右侧列出同一行中的所有叶子，如图2-15(a)。最后，我们对叶子进行排序，如图 2 所示——15(b).
图 2-15 显示了随身行李重量的柱叶式显示。从图 2-15 中的茎叶显示中，我们看到两个袋子称重27一世b, 一个称重3一世b, 一个称重51一世b， 等等。我们看到大部分重量都在 30 磅范围内，只有两个重量小于10一世b, 六个结束了40一世b. 请注意，包含叶子的线条的长度给人的视觉印象是会出现横向直方图。
作为最后一步，我们需要指明比例。这通常通过指示由茎和叶表示的值来完成。
选择词干的数字组没有固定的规则。但是无论您选择哪个组，您都必须在数据集合中从小到大列出所有可能的词根。
统计代写|AP统计作业代写代考|Cowboys: Longevity
牛仔：长寿 真正的牛仔能活多久？一个答案可以在康妮·布鲁克斯（新墨西哥大学出版社）的《最后的牛仔》一书中找到。这本令人愉快的书对 1890 年左右德克萨斯州西部和新墨西哥州东南部的牛仔进行了深思熟虑的社会学研究。32 名牛仔的样本给出了以下几年的长寿：
(a) 对这些数据进行茎叶显示。
(b) 解释 考虑一下冯·李希霍芬男爵在他的《北美平原养牛》中的以下引言：“在最好的家庭的儿子中可以找到牛仔。事实可能是，大多数人都不是醉酒、赌博、快速拔枪和开枪的人。”
长寿的数据分布是否证明了这句话的可信度？
生态：栖息地湿地提供多种益处。它们为野生动物提供栖息地、美国商业鱼类的产卵场和可再生木材资源。在过去的 200 年里，美国失去了一半以上的湿地。环境年鉴给出了过去 200 年中每个州失去的湿地百分比。对于较低的 48 个州，每个州的湿地损失百分比如下：
46373642812073593550 87522427385639745631 2791469545230332835 352390728542595049 483860468750894967
对这些数据进行茎叶显示。一定要标明比例。百分比是如何分配的？分布是否有偏差？有没有差距？
Course Overview
AP Statistics is an introductory college-level statistics course that introduces students to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Students cultivate their understanding of statistics using technology, investigations, problem solving, and writing as they explore concepts like variation and distribution; patterns and uncertainty; and data-based predictions, decisions, and conclusions.
- AP Statistics Course OverviewThis resource provides a succinct description of the course and exam.PDF180.39 KB
- AP Statistics Course and Exam Description Walk-ThroughLearn more about the CED in this interactive walk-through.
- AP Statistics Course at a GlanceExcerpted from the AP Statistics Course and Exam Description, the Course at a Glance document outlines the topics and skills covered in the AP Statistics course, along with suggestions for sequencing.PDF585.66 KB
- AP Statistics Course and Exam DescriptionThis is the core document for this course. Unit guides clearly lay out the course content and skills and recommend sequencing and pacing for them throughout the year. The CED was updated in March 2021.PDF17.9 MB
- AP Statistics CED Errata SheetThis document details the updates made to the course and exam description (CED) in March 2021.PDF934.27 KB
- AP Statistics CED Scoring GuidelinesThis document details how each of the sample free-response questions in the course and exam description (CED) would be scored. This information is now in the online CED, but was not included in the binders teachers received in 2019.PDF245.87 KB
Course Content
Based on the Understanding by Design® (Wiggins and McTighe) model, this course framework provides a clear and detailed description of the course requirements necessary for student success. The framework specifies what students must know, be able to do, and understand, with a focus on three big ideas that encompass the principles and processes in the discipline of statistics. The framework also encourages instruction that prepares students for advanced coursework in statistics or other fields using statistical reasoning and for active, informed engagement with a world of data to be interpreted appropriately and applied wisely to make informed decisions.
The AP Statistics framework is organized into nine commonly taught units of study that provide one possible sequence for the course. As always, you have the flexibility to organize the course content as you like.
| Unit | Exam Weighting (Multiple-Choice Section) |
| Unit 1: Exploring One-Variable Data | 15%–23% |
| Unit 2: Exploring Two-Variable Data | 5%–7% |
| Unit 3: Collecting Data | 12%–15% |
| Unit 4: Probability, Random Variables, and Probability Distributions | 10%–20% |
| Unit 5: Sampling Distributions | 7%–12% |
| Unit 6: Inference for Categorical Data: Proportions | 12%–15% |
| Unit 7: Inference for Quantitative Data: Means | 10%–18% |
| Unit 8: Inference for Categorical Data: Chi-Square | 2%–5% |
| Unit 9: Inference for Quantitative Data: Slopes | 2%–5% |
Course Skills
The AP Statistics framework included in the course and exam description outlines distinct skills that students should practice throughout the year—skills that will help them learn to think and act like statisticians.
| Skill | Description | Exam Weighting (Multiple-Choice Section) |
| 1. Selecting Statistical Methods | Select methods for collecting and/or analyzing data for statistical inference. | 15%–23% |
| 2. Data Analysis | Describe patterns, trends, associations, and relationships in data. | 15%–23% |
| 3. Using Probability and Simulation | Explore random phenomena. | 30%–40% |
| 4. Statistical Argumentation | Develop an explanation or justify a conclusion using evidence from data, definitions, or statistical inference. | 25%–35% |
统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。统计代写|python代写代考
随机过程代考
在概率论概念中，随机过程是随机变量的集合。 若一随机系统的样本点是随机函数，则称此函数为样本函数，这一随机系统全部样本函数的集合是一个随机过程。 实际应用中，样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化，随机运动如布朗运动、随机徘徊等等。
贝叶斯方法代考
贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布，和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型，后验分布可以解析或近似，例如，马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要，包括点估计，如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外，所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。
广义线性模型代考
广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。
statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。
机器学习代写
随着AI的大潮到来，Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比，Machine Learning在其他领域也有着广泛的应用，因此这门学科成为不仅折磨CS专业同学的“小恶魔”，也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多，跨学科范围广，所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题，StudyGate专业导师团队都能为你轻松解决。
多元统计分析代考
基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量
时间序列分析代写
随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。 随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。
回归分析代写
多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。
MATLAB代写
MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。